SimAvatar: Simulation-Ready Avatars with Layered Hair and Clothing (2412.09545v2)

Abstract: We introduce SimAvatar, a framework designed to generate simulation-ready clothed 3D human avatars from a text prompt. Current text-driven human avatar generation methods either model hair, clothing, and the human body using a unified geometry or produce hair and garments that are not easily adaptable for simulation within existing simulation pipelines. The primary challenge lies in representing the hair and garment geometry in a way that allows leveraging established prior knowledge from foundational image diffusion models (e.g., Stable Diffusion) while being simulation-ready using either physics or neural simulators. To address this task, we propose a two-stage framework that combines the flexibility of 3D Gaussians with simulation-ready hair strands and garment meshes. Specifically, we first employ three text-conditioned 3D generative models to generate garment mesh, body shape and hair strands from the given text prompt. To leverage prior knowledge from foundational diffusion models, we attach 3D Gaussians to the body mesh, garment mesh, as well as hair strands and learn the avatar appearance through optimization. To drive the avatar given a pose sequence, we first apply physics simulators onto the garment meshes and hair strands. We then transfer the motion onto 3D Gaussians through carefully designed mechanisms for each body part. As a result, our synthesized avatars have vivid texture and realistic dynamic motion. To the best of our knowledge, our method is the first to produce highly realistic, fully simulation-ready 3D avatars, surpassing the capabilities of current approaches.

• The paper introduces SimAvatar, a novel framework that generates layered, simulation-ready 3D human avatars from text using 3D Gaussians and diffusion models.
• The layered representation of body, hair, and garments enables realistic physics-based simulations, accurately capturing dynamic deformations such as wrinkles and flowing hair.
• SimAvatar produces simulation-ready avatars compatible with physics simulators, offering realistic motion effects crucial for applications in VR, digital media, and entertainment.

SimAvatar: Simulation-Ready Avatars with Layered Hair and Clothing

The research presented in "SimAvatar: Simulation-Ready Avatars with Layered Hair and Clothing" introduces a novel framework designed to generate 3D human avatars that are both realistic and ready for simulation. The framework addresses critical challenges in text-driven human avatar generation, namely the representation of complex avatar features such as hair and garments in a form that is compatible with existing simulation technologies.

The proposed system, SimAvatar, employs a two-stage framework that leverages the text-conditioned generative capabilities of 3D Gaussians combined with advanced diffusion models. This integration effectively translates descriptive text prompts into detailed 3D avatars with distinct layers for hair, clothing, and body. A consequential advancement made by this paper is the use of 3D Gaussians to enable realistic texture modeling while maintaining simulation readiness. This is particularly noteworthy as prior approaches using implicit methods such as NeRF-based representations faced difficulties in animation due to entangled geometries and non-watertight meshes.

Key Contributions

1. Layered Structure for Animation: The framework allows the separate representation and simulation of the body, hair, and garment layers. This layered approach enables more accurate physics-based simulations, which better capture dynamic and pose-dependent deformations such as garment wrinkles and flowing hair.
2. Generative Model Architecture: SimAvatar introduces a new generative model design where 3D Gaussians are aligned with foundational diffusion models to capture realistic textures and geometry detail. This model accommodates diverse text inputs to intuitively synthesize avatars with various identities and styles of garments and hairstyles.
3. Simulation-Ready Outputs: The framework uniquely synergizes traditional physics simulators with its avatars’ 3D Gaussian-enhanced geometries, offering realistic motion effects that are crucial for applications beyond static 3D model generation, such as virtual reality and digital media production.
4. Optimization Process: The authors implement a Score Distillation Sampling (SDS) based optimization process which ensures the appearance and texture quality even within the constraints and demands of real-time simulations.

The results of the paper underline the impact of a nuanced understanding of avatar dynamics. The method is reportedly the first of its kind in producing fully simulation-ready 3D avatars featuring independent layers, extending current capabilities by allowing for both detailed static presentation and sophisticated animated behavior.

Implications and Future Directions

Pragmatically, this work marks a significant contribution to fields such as digital entertainment, virtual fashion, and telepresence applications by providing a scalable method to produce customized 3D avatars. The authenticity with which avatars are presented promises considerable improvements in user immersion and engagement.

Theoretically, SimAvatar provides an insightful approach to merging text-based generative models with physical simulation, paving the way for further exploration into more complex body-garment-hair simulations. Future developments could explore optimization techniques for even more intricate interactions and simulations beyond those demonstrated.

The framework's reliance on detailed and separate treatment of avatar components could be augmented with further research into joint handling of accessories and other avatar embellishments. There is also potential in expanding beyond the existing dataset limits—which currently constraint diversity—to improve adaptability of the model to a wider array of human phenotypes and fashion styles.

In summary, SimAvatar presents a methodologically rigorous advance in the construction and simulation of 3D avatars, promoting a body of research that supports increased fidelity in virtual representations of people, and effectively setting a new bar for future research in AI-driven human avatar synthesis.